Geographic Patterns Research Articles

Transformation processes of total suspended solids and dissolved organic matter in rivers: Influences of different land use sources and degradation processes.

The riverine dissolved organic matter (DOM) pool constitutes the largest and most dynamic organic carbon reservoir within inland aquatic systems. Human activities significantly alter the distribution of organic matter (OM) in rivers, thereby affecting the availability of DOM. However, the impact of total suspended solids (TSS) on DOM under anthropogenic influence remains insufficiently elucidated. This study employed Fourier transform ion cyclotron resonance mass spectrometry, DOC characterization, and incubation experiments to investigate how land use and degradation processes influence TSS-DOM transformation in rivers. Our findings revealed that geographical patterns cause significant variations in both DOM composition and TSS content. Anthropogenic impacts led to an increase in autochthonous TSS content and an enhanced relative intensity (RI) of nitrogen (N)- and sulfur (S)-containing compounds in riverine DOM. The presence of TSS increased the bioavailability of DOM from 29.97% to 33.57%. However, during both photodegradation and combined degradation processes, the presence of TSS reduced the bioavailability of DOM. The degradation rate constant (k) of DOM decreased as degradation time increased. The k values were significantly correlated with the CHO components in natural rivers and with N- and S-containing components in human-influenced rivers. The degradation rates of DOC under different land uses were 0.05±0.04 d-1, 0.07±0.06 d-1, and 0.08±0.06 d-1 in forested, urban, and cropland-influenced rivers, respectively. The content of aliphatic compounds and the number of CHOS molecules in TSS-containing water were higher than in TSS-free water during the combined process of photochemical and microbial degradation, while the saturation and aromaticity of the compounds were lower. The characteristics of autochthonous DOM were more pronounced under the influence of TSS photorespiration. During drinking water disinfection, these small molecules derived from autochthonous TSS may contribute to an increase in disinfection by-products (DBPs) in drinking water. This study enhanced our understanding of how changes in autochthonous TSS content, driven by geographical heterogeneity and human activities, influence the biogeochemical processes of DOM in water, as well as the underlying molecular mechanisms and implications for water quality safety.

Impacts of anthropogenic disturbances on antibiotic resistomes in biological soil crusts on the Qinghai-Tibetan Plateau.

Biological soil crusts (BSCs) are the main landscape on the Qinghai-Tibetan Plateau and an ecological indicator of human disturbance. Information about antibiotic resistomes in BSCs on the Qinghai-Tibetan Plateau can provide baseline for the risk assessment and management of resistomes and yet to be explored. This work investigated the profiles and geographic patterns of antibiotic resistomes in BSCs along the Lhasa River and their response to anthropogenic activities for the first time. Various antibiotic resistance genes (ARGs) were widely distributed in BSCs, but had relatively lower detection frequency and abundance comparing to soils from human disturbed sites. ARGs profiles in BSCs were separated by altitude from 3860 to 3880m, possibly attributing to the difference in anthropogenic activities. Above 3860m, resistomes exhibited lower abundance including total ARGs, aadA, blaSFO and tnpA-04 owing to the rare human activities; at human disturbed sites with altitude <3860m, the detection frequency and relative abundance of tetG02, oprJ, qacEdelta1-01, and ARGs with the mechanism of efflux pump were higher and viewed as potential indicators of human activities. Anthropogenic activities potentially promoted the horizontal gene transfer of ARGs in BSCs at human disturbed sites from co-occurrence network analysis. Our findings provided fundamental information of antibiotic resistomes in BSCs on the Qinghai-Tibetan Plateau, and unraveled possible mechanisms of human disturbance in shaping antibiotic resistomes.

Association of a Liver Allocation Policy Change With Domestic Travel for Liver Transplantation.

In 2020, liver allocation policy in the United States was changed to allow for broader organ sharing, which was hypothesized to reduce patient incentives to travel for transplant. Our objective was to describe patterns of travel for domestic liver transplant pre- and post-acuity circle (AC) implementation. Incident adult liver transplant listings between August 16, 2016, and February 3, 2020 (pre-AC) or June 13, 2020, and December 3, 2023 (post-AC) were obtained from the Scientific Registry of Transplant Recipients. We used previously defined geographic catchment areas to classify patients as (1) no travel, (2) travel to a neighboring region, and (3) travel beyond a neighboring region. We used multinomial logistic regression to identify characteristics associated with travel and cause-specific hazards modeling to estimate the association between travel and time to deceased donor transplant, stratified by model for end-stage liver disease (MELD) score and AC era. Among 83 033 liver candidates, 76% were listed in their home region. Black race, lower educational attainment, increased neighborhood social deprivation, and Medicaid were significantly associated with decreased odds of traveling beyond a neighboring region. After AC, traveling beyond a neighboring region was associated with an increased hazard of transplant for patients with a MELD score <15 (cause-specific hazard ratio [csHR]: 1.25; 95% confidence interval [CI], 1.11-1.40), MELD score 15-24 (csHR: 1.19; 95% CI, 1.07-1.31), and MELD score 25-34 (csHR: 1.15; 95% CI, 1.01-1.32). Travel frequency, geographic patterns of travel, and characteristics associated with travel were largely unchanged after AC. Changes to allocation policy alone may not equalize patient means or desire to travel for transplant care.

Genomic evidence for demographic fluctuations, genetic burdens and adaptive divergence in fourfinger threadfin Eleutheronema rhadinum

Abstract Declining populations and bottlenecks lead to the accumulation of deleterious mutations in fish populations. These processes also trigger genetic purging, which is a key genetic factor in reducing the deleterious burdens and increasing population viability. However, there is a lack of empirical evidence on the interaction between demographic history and the genome-wide pattern of deleterious variations. Here, we generated genome resequencing data of Eleutheronema rhadinum from China and Thailand, representing the major distribution of the species’ southern regions. E. rhadinum had exceptionally low genome-wide variability and experienced dramatic population expansions followed by continuous declines. The geographical divergence, which occurred ~ 23,000 years ago, shaped different demographic trajectories and generated different regional patterns of deleterious mutations in China and Thailand populations. Several lines of evidence revealed that this geographical pattern of deleterious mutation was driven by the purging of highly deleterious mutations. We showed that purifying selection had inbreeding-associated fitness costs and was more efficient against missense mutations in the Thailand population, which had the lowest genetic burden of homozygous deleterious mutations. Multiple evolutionarily conserved protein domains were disrupted by the loss-of-function mutations, posing a high probability of gene functionality elimination. Moreover, thermal and salinity genes (Trpm3, Nek4, Gtf2f2, Cldn14) were identified in genomic divergence regions of E. rhadinum among China and Thailand populations. Our findings highlight the importance of demographic history factors shaping the geographical patterns of deleterious mutations. The results serve to deepen our understanding of the adaptive evolution and divergence of E. rhadinum with implications for other marine fish.

Phylogenetic Analysis of Chandipura virus: Insights from a Preliminary Genomic Study

Chandipura virus (CHPV) is an arthropod-borne virus linked to encephalitis in humans, primarily in India. Its evolutionary dynamics and transmission pathways remain poorly understood due to limited genomic data. This study analyzed 23 publicly available CHPV genomes, including isolates from humans, sandflies, and a hedgehog, retrieved from GenBank. Phylogenetic analyses were conducted to explore host-specific and geographic evolutionary patterns. Phylogenetic analysis revealed distinct evolutionary lineages. Human-derived genomes collected in India between 2003 and 2024 formed a well-supported monophyletic clade, suggesting a unique evolutionary lineage. In contrast, sandfly-derived genomes exhibited diverse clustering patterns. Notably, Kenyan sandfly isolates from 2016–2017 were phylogenetically closer to human-derived sequences, suggesting possible shared evolutionary pressures. These findings provide preliminary insights into CHPV evolution and emphasize the need for enhanced genomic surveillance in both human and non-human populations. Expanding genomic data is essential to validate these observations and inform public health strategies.

Survey of metabolically essential trace metals in inland lakes and reservoirs across Canada: What constitutes a low metal system?

Many proteins depend on metals for proper functioning, yet there is little information on the distribution of low metal concentrations in freshwaters nor what constitutes ‘low’. Eight dissolved metals were surveyed in 2017 in 39 lakes and reservoirs across Canada. RDA analysis revealed Co, Cu, Ni and V in one group with higher concentrations on the Prairies and Fe and Mn in a second group with higher concentrations on the Prairies and the Boreal Shield in Ontario. Zn and Mo lacked geographic patterns. Concentrations ranged several fold. Monod growth kinetic parameters were explored for their potential to infer growth limitation. Metal concentrations at or below growth thresholds (Tm, &lt; 1 nmol L-1) should severely limit growth while concentrations between Tm and twice the half-saturation constant (2Km) will limit growth when other nutrients are in excess. Published and inferred Tm suggest that Co, Mo and Ni could have been low enough to occasionally limit growth in several Canadian oligotrophic lakes. There are too few published Km values to infer limitation above Tm.

Revealing the nature of soil liquefaction using machine learning

This study highlights the importance of consistently assessing soil and subsoil liquefaction susceptibility in construction projects through comprehensive evaluation and regular site investigations. Despite the abundance of geotechnical reports, many remain underutilized post-project completion, while the literature contains numerous site-specific case studies. To address this gap, this study suggests integrating site-specific reports tailored to both the site and the region, aiming to extract valuable insights into liquefaction potential. Utilizing ensemble-based machine learning techniques, two databases of soil liquefaction were analyzed to predict the probability of liquefaction failure (PLF). The results reveal the superior accuracy of the Gradient Boosting Regressor (GBR) model, achieving an almost ideal accuracy in predicting PLF. Despite its generalization across diverse geographical patterns, the GBR model encounters limitations with insufficient experimental data within specific parameter ranges. To address this, a graphical user interface (GUI) was developed by leveraging data from previous liquefaction records to predict PLF. The GUI, which has been included as supplementary material, proved to be a useful tool for liquefaction risk assessment, and its predictive capabilities make it invaluable for both practical applications and educational purposes. This empowers engineers, urban planners, and decision-makers to make informed decisions and implement proactive measures for disaster mitigation and infrastructure development resilience.

Inter‐population variations of saccular otolith of Walton's mudskipper (Periophthalmus waltoni) of the Persian Gulf and Gulf of Oman

AbstractUnderstanding population structure is crucial for predicting species' responses to environmental change and elucidating evolutionary history. This study investigated the population structure of Periophthalmus waltoni in the Persian Gulf and Gulf of Oman using landmark‐based geometric morphometrics (GMM) and discrete wavelet transform (DWT) methods on sagittal otoliths. The objectives were to (i) examine otolith shape variation across different populations and (ii) compare GMM and DWT methods for resolving population differences. Both methods distinguished populations based on general otolith shape, with DWT revealing finer details due to its high accuracy in detecting otolith margins. Interestingly, populations from Sistan‐Baluchestan and Hormozgan, despite geographical separation, showed minimal variation in shape, suggesting environmental factors may influence otolith morphology. Distinct otolith shapes in the Khuzestan population, likely due to geographic isolation, may be influenced by the mangrove forests near Qeshm Island acting as barriers to larval dispersal. This aligns with molecular data and indicates that the relatively young eastern Persian Gulf may also contribute to these differences. Our findings revealed distinct geographical patterns in otolith shape, highlighting the influence of local environmental factors and larval dispersal on population differentiation. These results provide valuable insights into P. waltoni's population structure and evolutionary history, advancing our understanding of its adaptation to the diverse environmental conditions of the Persian Gulf and Oman Sea.

Morphological variation in the postabdomen of Camptocercus dadayi Stingelin, 1913 (Cladocera: Anomopoda: Chydoridae)

We investigated the morphological variations of Camptocercus dadayi Stingelin, 1913 in different populations across South America (Argentina, Brazil, Chile, and Colombia) under optical microscope. Significant variations were observed in the morphology of the postabdomen, especially in the patterns of the marginal denticles, lateral fascicles and terminal claw. The variations follow a geographic pattern throughout South America. These variations suggest morphologic diversity in the species, highlighting the importance of variation studies for the accurate identification and delimitation of species within Camptocercus.

Phylogenetic relationships of Smilax astrosperma (Smilacaceae) with other Smilax species and its potential distribution patterns in karst areas of south China

The genus Smilax is adapted for growth in karst areas of south China, likely because of the karst habitat heterogeneity of the area and climate factors. However, the phylogenetic relationships among different species in this genus and impact of environmental changes on its geographic distribution patterns remain unclear. Therefore, we assembled the 11 chloroplast genomes of the genus Smilax and reconstructed its phylogeny using the maximum‐likelihood method to confirm the phylogenetic relationships of Smilax astrosperma among 28 other Smilax genus species. In addition, a maximum entropy (MaxEnt) model was applied to evaluate and predict the potential distribution of S. astrosperma using bioclimatic variables, soil environment, elevation, slope degree and aspect, and human footprints under present and predicted future climate scenarios. The results show that S. astrosperma has a close relationship with S. glabra in the genus Smilax. The average temperature of the coldest quarter is a crucial factor influencing the distribution of S. astrosperma in karst areas. The potential distribution range of S. astrosperma increases and decreases under the SSP1‐2.6 and SSP5‐8.5 scenarios, respectively. These findings highlight that S. astrosperma and other species of the karst areas of south China need special attention under climate warming since both warming itself and extreme weather events threaten their survival.

The Earnings and Labor Supply of U.S. Physicians

Abstract Is government guiding the invisible hand at the top of the labor market? We use new administrative data to measure physicians’ earnings and estimate the influence of healthcare policies on these earnings, physicians’ labor supply, and allocation of talent. Combining the administrative registry of U.S. physicians with tax data, Medicare billing records, and survey responses, we find that physicians’ annual earnings average $350,000 and comprise 8.6% of national healthcare spending. Business income comprises one-quarter of earnings and is systematically underreported in survey data. Earnings increase steeply early in the career, and there are major differences across specialties, regions, and firm sizes. The geographic pattern of earnings is unusual compared with other workers. We argue that these patterns reflect policy choices to subsidize demand for physician care, amplified by restrictions on physician entry, especially in certain specialties. Health policy has a major impact on the margin: 25% of physician fee revenue driven by Medicare reimbursements accrues to physicians personally. Physicians earn 8% of public money spent on insurance expansion. These policies in turn affect the type and quantity of medical care physicians supply, retirement timing, and the allocation of talent across specialties.

Unveiling geographical patterns of hierarchy in the Greek labor market network: toward a multilayer “status-polus” model

Abstract This article develops a comprehensive framework for understanding labor markets’ spatial configuration and development dynamics, across multiple spatial scales. It applies to the Greek Labor Market Network and delineates five geographical zones of topological similarity, proposing a “status-polus” model capturing the coexistence between spatial development patterns, structural units, administrative scale, intermodality, and their underlying economic geography theories, by distance. The analysis reveals hierarchical structural variations, a transformation from continuous to point spatial patterns; a reduction and escalation of structural units; a U-shaped rule describing the hubs participation; and the challenges faced by insular regions to integrate this labor market.

Current Enzooticity of Dirofilaria immitis and Angiostrongylus vasorum in Central and Southern Italy.

Dirofilaria immitis and Angiostrongylus vasorum are major parasitic nematodes of dogs. Many environmental and phenological changes have recently modified their geographic patterns in many countries; thus, this study has updated the distribution of D. immitis and A. vasorum in dog populations of selected regions of Central and Southern Italy. Also, collateral data on other endoparasites affecting the study population have been collected. Blood and fecal samples collected from 2000 dogs were tested using Knott's test and copromicroscopy (i.e., Baermann's and fecal flotation tests), respectively. Binomial logistic regression was performed to evaluate statistically significant associations between positivity for D. immitis and/or A. vasorum and potential risk factors. Overall, 35 (1.7%) and 62 (3.1%) dogs were positive for microfilariae of D. immitis and first stage larvae (L1) of A. vasorum, respectively, while 3 (0.1%) were co-infected by both nematodes. Microfilariae of Dirofilaria repens were found in 148 (7.4%) dogs, while at the flotation, eggs of Ancylostomatidae, Trichuris vulpis, and ascarids were found in the feces of 323 (16.5%), 249 (12.4%), and 172 (8.6%), dogs, respectively. Overall, 217 (10.8%) and 44 (2.2%) dogs were positive for eggs of Capillaria aerophila and Capillaria boehmi. The presence of cardiorespiratory clinical signs or non-specific signs, history of travel, and an age of >4 years old were significantly associated with positivity for D. immitis, while A. vasorum was significantly recorded in dogs with cardiorespiratory signs, or with a history of mollusk ingestion or permanent outdoor housing. These results confirm that D. immitis is enzootic in the investigated regions of Central and Southern Italy, even where it was rare/undetected until recently. Indeed, although some dogs positive for D. immitis had a history of travel in enzootic areas, the majority of them were never moved, indicating that they acquired the parasite in the region where they live. Additionally, A. vasorum is stably enzootic in the study areas, as also are other extraintestinal nematodes (i.e., D. repens and C. aerophila) that are more frequently detected today than in the past. A high level of vigilance and routine parasitological screening are necessary, considering the high prevalence of intestinal parasites in owned dogs that are also co-infected by respiratory parasites. The implementation of chemoprevention against D. immitis in dogs living in the examined area should be encouraged.

Geographic factors associated with SARS-CoV-2 prevalence during the first wave − 6 districts in Zambia, July 2020

BackgroundGeographical factors can affect infectious disease transmission, including SARS-CoV-2, a virus that is spread through respiratory secretions. Prioritization of surveillance and response activities during a pandemic can be informed by a pathogen’s geographical transmission patterns. We assessed the relationship between geographical factors and SARS-CoV-2 prevalence in Zambia.MethodsWe did a cross-sectional study of SARS-CoV-2 prevalence in six districts in July 2020, which was during the upslope of the first wave in Zambia. In each district, 16 Standard Enumeration Areas (SEAs) were randomly selected and 20 households from each SEA were sampled. The SEA PCR prevalence was calculated as the number of persons testing PCR positive for SARS-CoV-2 in the SEA times the individual sampling weight for the SEA divided by the SEA population. We analysed SEA geographical data for population density, socioeconomic status (SES) (with lower scores indicating reduced vulnerability), literacy, access to water, and sanitation, and hygiene (WASH) factors. Gaussian conditional autoregressive (CAR) models and Generalised estimating equations (GEE) were used to measure adjusted prevalence Ratios (aPRs) and 95% confidence intervals (CIs) for SARS-CoV-2 prevalence with geographical factors, after adjusting for clustering by district, in R.ResultsOverall, the median SARS-CoV-2 prevalence in the 96 SEAs was 41.7 (Interquartile range (IQR), 0.0-96.2) infections per 1000 persons. In the multivariable CAR analysis, increasing SES vulnerability was associated with lower SARS-CoV-2 prevalence (aPR) = 0.85, 95% CI: 0.78–0.94). Conversely, urban SEAs and poor access to WASH were associated with a higher SARS-CoV-2 prevalence (aPR = 1.73, 95% CI: 1.46–2.03, No soap: aPR = 1.47, 95% CI: 1.05–2.05, households without piped water: aPR = 1.32, 95% CI: 1.05–1.65, 30 min to fetch water: aPR = 23.39, 95% CI: 8.89–61.52). Findings were similar in the multivariable GEE analysis.ConclusionsSARS-CoV-2 prevalence was higher in wealthier, urban EAs, with poor access to WASH. As this study was conducted early in the first wave could have impacted our findings. Additional analyses from subsequent waves could confirm if these findings persist. During the beginning of a COVID-19 wave in Zambia, surveillance and response activities should be focused on urban population centres and improving access to WASH.

The Geographical Distribution of Global Biobanks.

This study aimed to comprehensively review the global biobanks to visualize their geographical distribution. The protocol for this review consisted of the following steps: i. Developing a search strategy to identify biobanks from each continent, ii. Defining variables (such as tissue-based, cell-based, and gene-based biobanks) and organizing them in Excel sheets for data collection, iii. Collecting data, iv. Removing duplicate and invalid entries, v. Structuring the database, and vi. Analyzing the data. MATLAB software was utilized for data analysis and chart plotting. Data on global biobanks aimed to collected through targeted searches of databases, publications, and registries using predefined variables such as biobank type, location, and accessibility. The data were organized, cleaned to remove duplicates, and analyzed using MATLAB to visualize geographical distribution and prevalence patterns. Tissue and cell-based, tissue-based, and cellbased biobanks were the most common type of global biobanks with a prevalence of 30.4, 27.93, and 25.15%. United Kingdom (n=78, P=43.09%), Canada (n=43, P=23.75%), and the United States (n=33, P=18.23%) were the countries with a higher frequency of tissue-based biobanks (domain frequency: 1-78; 0.55-43.09%). However, tissue and genebased biobanks had the most minor frequency and were only in two countries of Spain (n=1, P=25%) and the United Kingdom (n=3, P=75%). The results of this study indicate that the feasibility of designing and conducting biobanks varies by type. Tissue and cell-based biobanks were found to be more prevalent, followed by tissue-based, cell-based, cell and gene-based, tissue, cell, and gene-based, gene-based, and finally, tissue and gene-based biobanks. This study represents the initial step in creating a global database by identifying all types of biobanks worldwide.

Exploring infodemiology: unraveling the intricate relationships among stress, headaches, migraines, and suicide through Google Trends analysis.

Google Trends has emerged as a vital resource for understanding public information-seeking behavior. This study investigates the interconnected search trends of stress, headaches, migraines, and suicide, highlighting their relevance to public health and mental well-being. By employing infodemiology, the study explores temporal and geographical patterns in search behavior and examines the impact of global events like the COVID-19 pandemic. Data mining was conducted using Google Trends for the search terms "stress," "headache," "migraine," and "suicide." Relative Search Volume (RSV) data from October 2013 to October 2023 was collected and adjusted for time and location. Statistical analyses, including Pearson correlation tests, linear regression, and seasonal Mann-Kendall tests, were applied to identify correlations, trends, and seasonal variations. Geographical differences were also analyzed to understand regional disparities. Significant correlations were observed among the search terms, with "migraine" and "suicide" showing the strongest association. Seasonal variations revealed a peak in search volumes during winter months. Geographical analysis highlighted consistently high RSV in the Philippines for all terms. During the COVID-19 pandemic, searches for stress, headaches, and migraines showed notable increases, reflecting heightened public interest in mental health-related topics during this period. The study underscores the interconnected nature of stress, headaches, migraines, and suicide in public search behavior. Seasonal patterns and regional variations emphasize the need for targeted interventions. The observed surge in search volume during the COVID-19 pandemic highlights the profound impact of global crises on mental health and the importance of timely public health responses. Google Trends provides valuable insights into the public's interest in health-related topics, demonstrating the intricate relationship between stress, headaches, migraines, and suicide. The findings highlight the need for increased mental health awareness and interventions, particularly during times of heightened stress. Further research is essential to develop strategies that mitigate the impact of these stressors on public health.

Bias in mobility datasets drives divergence in modeled outbreak dynamics

BackgroundDigital data sources such as mobile phone call detail records (CDRs) are increasingly being used to estimate population mobility fluxes and to predict the spatiotemporal dynamics of infectious disease outbreaks. Differences in mobile phone operators’ geographic coverage, however, may result in biased mobility estimates.MethodsWe leverage a unique dataset consisting of CDRs from three mobile phone operators in Bangladesh and digital trace data from Meta’s Data for Good program to compare mobility patterns across these sources. We use a metapopulation model to compare the sources’ effects on simulated outbreak trajectories, and compare results with a benchmark model with data from all three operators, representing around 100 million subscribers across the country.ResultsWe show that mobility sources can vary significantly in their coverage of travel routes and geographic mobility patterns. Differences in projected outbreak dynamics are more pronounced at finer spatial scales, especially if the outbreak is seeded in smaller and/or geographically isolated regions. In some instances, a simple diffusion (gravity) model was better able to capture the timing and spatial spread of the outbreak compared to the sparser mobility sources.ConclusionsOur results highlight the potential biases in predicted outbreak dynamics from a metapopulation model parameterized with non-population representative data, and the limits to the generalizability of models built on these types of novel human behavioral data.

The Biorepository and Integrative Genomics resource for inclusive genomics: insights from a diverse pediatric and admixed cohort.

The Biorepository and Integrative Genomics (BIG) Initiative in Tennessee has developed a pioneering resource to address gaps in genomic research by linking genomic, phenotypic, and environmental data from a diverse Mid-South population, including underrepresented groups. We analyzed 13,152 genomes from BIG and found significant genetic diversity, with 50% of participants inferred to have non-European or several types of admixed ancestry. Ancestry within the BIG cohort is stratified, with distinct geographic and demographic patterns, as African ancestry is more common in urban areas, while European ancestry is more common in suburban regions. We observe ancestry-specific rates of novel genetic variants, which are enriched for functional or clinical relevance. Disease prevalence analysis linked ancestry and environmental factors, showing higher odds ratios for asthma and obesity in minority groups, particularly in the urban area. Finally, we observe discrepancies between self-reported race and genetic ancestry, with related individuals self-identifying in differing racial categories. These findings underscore the limitations of race as a biomedical variable. BIG has proven to be an effective model for community-centered precision medicine. We integrated genomics education, and fostered great trust among the contributing communities. Future goals include cohort expansion, and enhanced genomic analysis, to ensure equitable healthcare outcomes.

From threats to solutions: A literature review of climate adaptation in anadromous salmon and trout

AbstractUnderstanding the evolutionary responses of anadromous salmon and trout to climate change is critical for effective conservation planning. In this study, we conducted a comprehensive review of literature published from 2010 to 2020 to synthesize current knowledge on climate impacts to these fish populations. Specifically, we focused on 199 papers that explored evolutionary processes in response to changing environmental conditions. Our analysis revealed several key themes, including the interwoven influences of climate and human activities on genetic variation, phenotypic traits, and population dynamics. We found that geographic patterns in genetic diversity are closely linked to climatic gradients, highlighting the importance to conservation strategies of variation in existing adaptive capacity. Additionally, temporal trends in phenology, maturation age, and fecundity indicate ongoing evolutionary and plastic responses to climate change. Importantly, human activities were identified as significant drivers of maladaptation in anadromous salmon and trout populations. We emphasize the need for targeted monitoring of specific evolutionary processes to mitigate the loss of genetic diversity and enhance adaptive capacity. Our study underscores the importance of identifying and protecting areas of high genetic diversity and rare genes, particularly in regions projected to experience rapid climatic shifts. In conclusion, our findings identify strengths and gaps in the research investigating the role of evolutionary dynamics in the face of climate change. By capitalizing on new tools for sequencing, genomic analysis, and automated field data collection, we can establish baselines for tracking evolutionary responses to climate change. Better integration of evolutionary processes into projections of future climate impacts will lead to more effective strategies to ensure the long‐term resilience of these iconic fish species and other wildlife.

Population variability across geographical ranges: perspectives and challenges.

Populations fluctuate over time and across geographical space, and understanding how different factors contribute to population variability is a central goal in population ecology. There is a particular interest in identifying trends of population variability within geographical ranges as population densities of species can fluctuate substantially across geographical space. A common assumption is that populations vary more near species geographical range edges because of unsuitable environments and higher vulnerability to environmental variability in these areas. However, empirical data rarely support this expectation, suggesting that population variability is not related to its position within species geographical ranges. We propose that performance curves, which describe the relationship between population growth rates and environmental conditions, can be used to disentangle geographical patterns of population variability. Performance curves are important for understanding population variability because populations fluctuate more in locations where they have lower growth rates owing to unsuitable environmental conditions. This is important for the assessment of these geographical patterns in population variability because geographical edges often do not reflect environmental edges. Considering species performance curves when evaluating geographical patterns of population variability would also allow researchers to detect populations that are more susceptible to future changes in environmental conditions.